Optimizing Image Reconstruction in SENSE Using GPU

Parallel magnetic resonance imaging (MRI) (pMRI) uses multiple receiver coils to reduce the MRI scan time. To accelerate the data acquisition process in MRI, less amount of data is acquired from the scanner which leads to artifacts in the reconstructed images. SENSitivity Encoding (SENSE) is a reconstruction algorithm in pMRI to remove aliasing artifacts from the undersampled multi coil data and recovers fully sampled images. The main limitation of SENSE is computing inverse of the encoding matrix. This work proposes the inversion of encoding matrix using Jacobi singular value decomposition (SVD) algorithm for image reconstruction on GPUs to accelerate the reconstruction process. The performance of Jacobi SVD is compared with Gauss–Jordan algorithm. The simulations are performed on two datasets (brain and cardiac) with acceleration factors 2, 4, 6 and 8. The results show that the graphics processing unit (GPU) provides a speed up to 21.6 times as compared to CPU reconstruction. Jacobi SVD algorithm performs better in terms of acceleration in reconstructions on GPUs as compared to Gauss–Jordan method. The proposed algorithm is suitable for any number of coils and acceleration factors for SENSE reconstruction on real time processing systems.     

Medical imaging using capacitive micromachined ultrasonic transducer arrays

We are investigating the use of capacitive micromachined ultrasonic transducers (cMUT's) for use in medical imaging. We propose an ultrasound probe architecture designed to provide volumetric ultrasound imaging from within an endoscope channel. A complete automated experimental system has been implemented for testing the imaging performance of cMUT arrays. This PC-based system includes custom-designed circuit boards, a software interface, and resolution test phantoms. We have already fabricated 1D and 2D cMUT arrays, and tested the pulse-echo imaging characteristics of 1D arrays. Beamforming and image formation algorithms that aim to reduce the complexity of data acquisition hardware are tested via numerical simulations and using real data acquired from our system.     

Triply-Logarithmic Parallel Upper and Lower Bounds for Minimum and Range Minima over Small Domains

We consider the problem of computing the minimum ofnvalues, and several well-known generalizations [prefix minima, range minima, and all nearest smaller values (ANSV)] for input elements drawn from the integer domain [1···s], wheres ≥ n. In this article we give simple and efficient algorithms for all of the preceding problems. These algorithms all takeO(log log log s) time using an optimal number of processors andO(ns^ε) space (for constant ε < 1) on the COMMON CRCW PRAM. The best known upper bounds for the range minima and ANSV problems were previouslyO(log log n) (using algorithms for unbounded domains). For the prefix minima and for the minimum problems, the improvement is with regard to the model of computation. We also prove a lower bound of Ω(log log n) for domain sizes = 2^{Ω(log n log log n)}. Since, forsat the lower end of this range, log log n = Ω(log log log s), this demonstrates that any algorithm running ino(log log log s) time must restrict the range ofson which it works.     

A sharp threshold for minimum bounded-depth and bounded-diameter spanning trees and Steiner trees in random networks

In the complete graph on n vertices, when each edge has a weight which is an exponential random variable, Frieze proved that the minimum spanning tree has weight tending to ζ(3) = 1/1³ + 1/2³ + 1/3³ +… as n → ∞. We consider spanning trees constrained to have depth bounded by k from a specified root. We prove that if k ≥ log₂ logn+ω(1), where ω(1) is any function going to ∞ with n, then the minimum bounded-depth spanning tree still has weight tending to ζ(3) as n → ∞, and that if k < log₂ logn, then the weight is doubly-exponentially large in log₂ logn ? k. It is NP-hard to find the minimum bounded-depth spanning tree, but when k≤log₂ logn?ω(1), a simple greedy algorithm is asymptotically optimal, and when k ≥ log₂ logn+ω(1), an algorithm which makes small changes to the minimum (unbounded depth) spanning tree is asymptotically optimal. We prove similar results for minimum bounded-depth Steiner trees, where the tree must connect a specified set of m vertices, and may or may not include other vertices. In particular, when m=const×n, if k≥log₂ logn+ω(1), the minimum bounded-depth Steiner tree on the complete graph has asymptotically the same weight as the minimum Steiner tree, and if 1 ≤ k ≤ log₂ logn?ω(1), the weight tends to $(1 - 2^{ - k} )\sqrt {8m/n} \left[ {\sqrt {2mn} /2^k } \right]^{1/(2^k - 1)}$ in both expectation and probability. The same results hold for minimum bounded-diameter Steiner trees when the diameter bound is 2k; when the diameter bound is increased from 2k to 2k+1, the minimum Steiner tree weight is reduced by a factor of $2^{1/(2^k - 1)}$ .     

Random Čech complexes on Riemannian manifolds

In this paper we study the homology of a random ?ech complex generated by a homogeneous Poisson process in a compact Riemannian manifold M. In particular, we focus on the phase transition for “homological connectivity” where the homology of the complex becomes isomorphic to that of M. The results presented in this paper are an important generalization of 7 , from the flat torus to general compact Riemannian manifolds. In addition to proving the statements related to homological connectivity, the methods we develop in this paper can be used as a framework for translating results for random geometric graphs and complexes from the Euclidean setting into the more general Riemannian one.     

MRI findings of intraosseous lipoma: case report

Intraosseous lipoma is one of the rarest benign bone tumors and usually presents in the fourth to fifth decades. Except for minor symptoms, they are usually asymptomatic. Metaphysis of long bones, especially proximal parts of femur and fibula, are involved in most cases. We present intraosseous lipoma to discuss the benefits of MRI and contrast-enhanced MRI findings for differential diagnosis.     

Time-slot interchange of 40 Gbits/s variable length optical packets using conversion-dispersion-based tunable delays

We demonstrate tunable time-slot interchange of 40 Gbits/s optical data packets using a conversion-dispersion-based tunable optical delay element. Odd and even data packets are extracted from an input signal, delayed relative to one another in a highly dispersive medium, and then multiplexed back together. Tunability is demonstrated by operating with two different packet lengths, 182 and 288 bits/packet, and a bit error rate of <10(-9) is achieved.